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Skewness is a way to explain how the data is spread out around the mean. It tells us whether the data is more falling on one side or the other rather than being distributed equally What does it indicate? Direction of Tilt- Skewness reflects if the data is more towards the right , If yes then its is (Positive skewness) or it is towards left then its is (Negative Skewness) Where is our mostly data - It shows where the most data points are positioned. If its is s case of Positive Skewness then data points are on the lower side. In negative Skewness- Data points are on the higher side Outliers- Skewness can also suggest if there are unusual values or outliers that are far from the rest of the data Types of Skewness Positive Skewness (Right- Skewed)- Here the Mean is moreover larger than the median because the higher value pulls it up Example= A batch of 20 candidates participated in Master black belt program . Most candidates scored between 70% -80% out of 100 %, only some of them obtained 95% or more. The point here is that Mean score will be coming 85% due to the high scores but most students actually scored around 70 - 80 %, which makes the Mean higher than the typical for the batch . As a result of which we might think the class did good overall than they actually did Negative Skewness (Left- Skewed)- Here mostly data points are on the higher side, however some low values pulls the average down. Example- Think of a small company where mostly people are getting 20000 - 40000 INR salary, whoever is working from long back older is earning 10000 INR. The point here is that Mean salary will be coming around 25000 INR because of some old people getting 10000 INR salary. Even though the mostly people are getting 30000 INR salary. As a result of which mostly people will look getting less salary than the actually get. Zero Skewness(- The distribution of datapoints is equally distributed) Example when we play Ludo and imagine that the dice is giving 3,4,5,6 repetitively for number so times we played. In this case Mean will be 4.5 and mode will also be 4 or 5. The point here is that dataset will be evenly distributed actually each number has come about the same number of times How it affects our Interpretation? Skewness is crucial to understand for correct data interpretation ensuring that statistical analyses give valid and reliable result. It also represents that datasets are evenly spread or not that actually impacts the mean, mode and median and other measures

ChatGPT response validated by Benchmark Six Sigma 1. Defects Use-Case: Automated Testing and Bug Detection AI chatbots can be integrated into the development pipeline to perform automated testing and detect bugs early. By using machine learning algorithms, chatbots can identify patterns and anomalies in code, reducing the occurrence of defects and ensuring higher quality software. 2. Overproduction Use-Case: Demand Forecasting AI chatbots can analyze historical data and predict future demand for IT services or products. This helps in aligning production with actual needs, preventing overproduction, and ensuring resources are used efficiently. 3. Waiting Use-Case: Instantaneous Support and Feedback AI chatbots can provide real-time support to developers and IT staff, answering queries and resolving issues immediately. This reduces downtime and waiting periods for human intervention, thus speeding up development and maintenance processes. 4. Non-Utilized Talent Use-Case: Knowledge Management and Skill Development AI chatbots can facilitate continuous learning and development by recommending training programs and resources based on individual skill gaps and career goals. This ensures that talent within the organization is fully utilized and continuously developed. 5. Transportation Use-Case: Digital Asset Management AI chatbots can manage and retrieve digital assets (such as code libraries, documentation, etc.) efficiently. By minimizing the time spent searching for resources, transportation waste is reduced, and development can proceed more smoothly. 6. Inventory Use-Case: Code Repository Optimization AI chatbots can monitor code repositories to ensure that only necessary and relevant code versions are maintained. This prevents the accumulation of obsolete or redundant code, optimizing inventory management. 7. Motion Use-Case: Streamlined Workflow Automation AI chatbots can automate repetitive tasks and streamline workflows, reducing the unnecessary motion involved in switching between tasks and tools. This allows developers to focus on more value-added activities. 8. Extra-Processing Use-Case: Code Review and Refactoring Assistance AI chatbots can assist in code reviews and suggest refactoring opportunities. By identifying and eliminating unnecessary steps or inefficient code, chatbots help in optimizing processing efforts and improving overall code quality. Conclusion By leveraging AI chatbots in these ways, IT product development and maintenance can become more efficient, cost-effective, and focused on delivering higher quality outputs with minimal waste.